Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "173" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 33 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 33 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460016 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.925641 | 14.964824 | 4.797171 | 5.250515 | 5.647150 | 6.625272 | 3.364289 | 6.573556 | 0.0356 | 0.0409 | 0.0035 | nan | nan |
| 2460015 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 13.727911 | 15.444999 | 4.971172 | 5.519401 | 5.885002 | 7.029751 | 3.616475 | 6.457206 | 0.0359 | 0.0427 | 0.0044 | nan | nan |
| 2460014 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.792157 | 13.031057 | 3.332044 | 3.718295 | 8.996295 | 10.499504 | 2.981106 | 5.292079 | 0.0345 | 0.0389 | 0.0030 | nan | nan |
| 2460013 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 13.495018 | 15.861574 | 5.017970 | 5.468860 | 6.006102 | 7.067579 | 4.811462 | 8.397562 | 0.0353 | 0.0405 | 0.0033 | nan | nan |
| 2460012 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.788417 | 14.918201 | 4.837056 | 5.305387 | 6.518111 | 7.792659 | 5.699775 | 10.717960 | 0.0361 | 0.0424 | 0.0038 | nan | nan |
| 2460011 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 14.210938 | 15.667696 | 6.332067 | 7.035993 | 13.605275 | 16.116769 | 4.228158 | 7.466068 | 0.0377 | 0.0442 | 0.0045 | nan | nan |
| 2460010 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 15.287174 | 17.496224 | 4.979861 | 5.922431 | 9.297669 | 10.558469 | 4.088243 | 7.370575 | 0.0383 | 0.0437 | 0.0039 | nan | nan |
| 2460009 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 14.281005 | 16.057957 | 5.838044 | 6.706346 | 7.380254 | 8.907406 | 4.749600 | 7.503202 | 0.0357 | 0.0427 | 0.0046 | nan | nan |
| 2460008 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 17.109317 | 19.154550 | 6.162332 | 7.199142 | 6.679488 | 7.791843 | 5.547774 | 7.066934 | 0.0423 | 0.0482 | 0.0048 | nan | nan |
| 2460007 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.804316 | 14.381802 | 4.831704 | 5.651822 | 5.974283 | 7.242260 | 4.618868 | 7.013848 | 0.0365 | 0.0439 | 0.0051 | nan | nan |
| 2459999 | digital_ok | 0.00% | 99.92% | 100.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.0337 | 0.0384 | 0.0045 | nan | nan |
| 2459998 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.885499 | 12.410224 | 4.044716 | 4.632747 | 8.091764 | 10.277069 | 3.053390 | 6.616153 | 0.0348 | 0.0400 | 0.0036 | nan | nan |
| 2459997 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.906658 | 13.599343 | 4.305248 | 5.076997 | 7.821157 | 9.694635 | 6.363011 | 11.086388 | 0.0360 | 0.0430 | 0.0046 | nan | nan |
| 2459996 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 13.258529 | 14.769406 | 5.816594 | 6.447266 | 7.371720 | 9.356253 | 2.380686 | 5.012671 | 0.0353 | 0.0406 | 0.0036 | nan | nan |
| 2459995 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 13.507582 | 14.570353 | 4.983914 | 5.720865 | 8.164597 | 9.549749 | 2.103784 | 4.710334 | 0.0384 | 0.0466 | 0.0053 | nan | nan |
| 2459994 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.912104 | 13.983725 | 4.168075 | 4.968011 | 7.867663 | 9.588283 | 2.910235 | 5.106389 | 0.0354 | 0.0416 | 0.0043 | nan | nan |
| 2459993 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 14.237015 | 13.197854 | 3.573787 | 4.298080 | 10.302141 | 10.963712 | 2.462531 | 4.641235 | 0.0346 | 0.0359 | 0.0019 | nan | nan |
| 2459991 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 15.223476 | 16.398387 | 3.942938 | 4.696564 | 9.285953 | 10.791371 | 2.512020 | 3.917358 | 0.0357 | 0.0410 | 0.0038 | nan | nan |
| 2459990 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.328455 | 13.641017 | 3.788542 | 4.447151 | 9.165915 | 11.075640 | 2.319336 | 3.905313 | 0.0371 | 0.0435 | 0.0044 | nan | nan |
| 2459989 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.114205 | 13.813549 | 3.350772 | 4.188045 | 8.091644 | 9.278402 | 1.571706 | 2.834776 | 0.0348 | 0.0406 | 0.0041 | nan | nan |
| 2459988 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 14.561951 | 16.348257 | 3.872712 | 4.510564 | 10.884203 | 13.241305 | 1.874393 | 3.353063 | 0.0349 | 0.0397 | 0.0037 | nan | nan |
| 2459987 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.026478 | 13.417325 | 3.904754 | 4.698766 | 6.505071 | 8.033250 | 4.765124 | 7.770903 | 0.0379 | 0.0437 | 0.0043 | nan | nan |
| 2459986 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 15.010907 | 16.788763 | 4.286565 | 4.991728 | 9.482713 | 11.248947 | 6.780664 | 11.316561 | 0.0351 | 0.0409 | 0.0040 | nan | nan |
| 2459985 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 13.852973 | 15.289979 | 4.022393 | 4.731149 | 7.318547 | 8.683600 | 4.036824 | 7.531977 | 0.0361 | 0.0412 | 0.0038 | nan | nan |
| 2459984 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 13.251489 | 14.857078 | 4.306914 | 5.026430 | 9.491630 | 12.110749 | 3.972101 | 6.357414 | 0.0406 | 0.0452 | 0.0036 | nan | nan |
| 2459983 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.872521 | 14.023249 | 3.893414 | 4.469364 | 9.399060 | 11.206263 | 5.376537 | 8.962277 | 0.0405 | 0.0450 | 0.0040 | nan | nan |
| 2459982 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.937333 | 11.409072 | 3.439956 | 3.984529 | 4.597960 | 5.281064 | 2.699990 | 3.498668 | 0.0397 | 0.0438 | 0.0038 | nan | nan |
| 2459981 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.929099 | 12.868144 | 3.957188 | 4.554481 | 10.565140 | 12.381817 | 2.558450 | 4.454818 | 0.0417 | 0.0461 | 0.0039 | nan | nan |
| 2459980 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.707243 | 12.484161 | 3.571514 | 4.282519 | 9.190324 | 10.853629 | 5.786891 | 6.253359 | 0.0410 | 0.0454 | 0.0039 | nan | nan |
| 2459979 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.191394 | 12.966230 | 3.113923 | 3.865897 | 9.061666 | 10.147491 | 2.805748 | 4.129467 | 0.0410 | 0.0434 | 0.0036 | nan | nan |
| 2459978 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.301152 | 13.225719 | 3.435726 | 4.159906 | 9.462059 | 11.006299 | 3.155195 | 5.198780 | 0.0376 | 0.0413 | 0.0036 | nan | nan |
| 2459977 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.688878 | 14.070386 | 3.610557 | 4.350849 | 9.331436 | 11.338970 | 2.543767 | 4.798720 | 0.0410 | 0.0469 | 0.0050 | nan | nan |
| 2459976 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.546616 | 13.629808 | 3.661035 | 4.337351 | 9.530805 | 10.885009 | 2.487174 | 3.908450 | 0.0384 | 0.0427 | 0.0038 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | N16 | digital_ok | nn Shape | 14.964824 | 14.964824 | 12.925641 | 5.250515 | 4.797171 | 6.625272 | 5.647150 | 6.573556 | 3.364289 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | N16 | digital_ok | nn Shape | 15.444999 | 15.444999 | 13.727911 | 5.519401 | 4.971172 | 7.029751 | 5.885002 | 6.457206 | 3.616475 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | N16 | digital_ok | nn Shape | 13.031057 | 12.792157 | 13.031057 | 3.332044 | 3.718295 | 8.996295 | 10.499504 | 2.981106 | 5.292079 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | N16 | digital_ok | nn Shape | 15.861574 | 13.495018 | 15.861574 | 5.017970 | 5.468860 | 6.006102 | 7.067579 | 4.811462 | 8.397562 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | N16 | digital_ok | nn Shape | 14.918201 | 12.788417 | 14.918201 | 4.837056 | 5.305387 | 6.518111 | 7.792659 | 5.699775 | 10.717960 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | N16 | digital_ok | nn Temporal Variability | 16.116769 | 14.210938 | 15.667696 | 6.332067 | 7.035993 | 13.605275 | 16.116769 | 4.228158 | 7.466068 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | N16 | digital_ok | nn Shape | 17.496224 | 15.287174 | 17.496224 | 4.979861 | 5.922431 | 9.297669 | 10.558469 | 4.088243 | 7.370575 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | N16 | digital_ok | nn Shape | 16.057957 | 14.281005 | 16.057957 | 5.838044 | 6.706346 | 7.380254 | 8.907406 | 4.749600 | 7.503202 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | N16 | digital_ok | nn Shape | 19.154550 | 19.154550 | 17.109317 | 7.199142 | 6.162332 | 7.791843 | 6.679488 | 7.066934 | 5.547774 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | N16 | digital_ok | nn Shape | 14.381802 | 12.804316 | 14.381802 | 4.831704 | 5.651822 | 5.974283 | 7.242260 | 4.618868 | 7.013848 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | N16 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | N16 | digital_ok | nn Shape | 12.410224 | 10.885499 | 12.410224 | 4.044716 | 4.632747 | 8.091764 | 10.277069 | 3.053390 | 6.616153 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | N16 | digital_ok | nn Shape | 13.599343 | 11.906658 | 13.599343 | 4.305248 | 5.076997 | 7.821157 | 9.694635 | 6.363011 | 11.086388 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | N16 | digital_ok | nn Shape | 14.769406 | 13.258529 | 14.769406 | 5.816594 | 6.447266 | 7.371720 | 9.356253 | 2.380686 | 5.012671 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | N16 | digital_ok | nn Shape | 14.570353 | 13.507582 | 14.570353 | 4.983914 | 5.720865 | 8.164597 | 9.549749 | 2.103784 | 4.710334 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | N16 | digital_ok | nn Shape | 13.983725 | 12.912104 | 13.983725 | 4.168075 | 4.968011 | 7.867663 | 9.588283 | 2.910235 | 5.106389 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | N16 | digital_ok | ee Shape | 14.237015 | 14.237015 | 13.197854 | 3.573787 | 4.298080 | 10.302141 | 10.963712 | 2.462531 | 4.641235 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | N16 | digital_ok | nn Shape | 16.398387 | 15.223476 | 16.398387 | 3.942938 | 4.696564 | 9.285953 | 10.791371 | 2.512020 | 3.917358 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | N16 | digital_ok | nn Shape | 13.641017 | 13.641017 | 12.328455 | 4.447151 | 3.788542 | 11.075640 | 9.165915 | 3.905313 | 2.319336 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | N16 | digital_ok | nn Shape | 13.813549 | 13.813549 | 12.114205 | 4.188045 | 3.350772 | 9.278402 | 8.091644 | 2.834776 | 1.571706 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | N16 | digital_ok | nn Shape | 16.348257 | 16.348257 | 14.561951 | 4.510564 | 3.872712 | 13.241305 | 10.884203 | 3.353063 | 1.874393 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | N16 | digital_ok | nn Shape | 13.417325 | 12.026478 | 13.417325 | 3.904754 | 4.698766 | 6.505071 | 8.033250 | 4.765124 | 7.770903 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | N16 | digital_ok | nn Shape | 16.788763 | 16.788763 | 15.010907 | 4.991728 | 4.286565 | 11.248947 | 9.482713 | 11.316561 | 6.780664 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | N16 | digital_ok | nn Shape | 15.289979 | 15.289979 | 13.852973 | 4.731149 | 4.022393 | 8.683600 | 7.318547 | 7.531977 | 4.036824 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | N16 | digital_ok | nn Shape | 14.857078 | 13.251489 | 14.857078 | 4.306914 | 5.026430 | 9.491630 | 12.110749 | 3.972101 | 6.357414 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | N16 | digital_ok | nn Shape | 14.023249 | 12.872521 | 14.023249 | 3.893414 | 4.469364 | 9.399060 | 11.206263 | 5.376537 | 8.962277 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | N16 | digital_ok | nn Shape | 11.409072 | 10.937333 | 11.409072 | 3.439956 | 3.984529 | 4.597960 | 5.281064 | 2.699990 | 3.498668 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | N16 | digital_ok | nn Shape | 12.868144 | 12.868144 | 11.929099 | 4.554481 | 3.957188 | 12.381817 | 10.565140 | 4.454818 | 2.558450 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | N16 | digital_ok | nn Shape | 12.484161 | 12.484161 | 11.707243 | 4.282519 | 3.571514 | 10.853629 | 9.190324 | 6.253359 | 5.786891 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | N16 | digital_ok | nn Shape | 12.966230 | 12.191394 | 12.966230 | 3.113923 | 3.865897 | 9.061666 | 10.147491 | 2.805748 | 4.129467 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | N16 | digital_ok | nn Shape | 13.225719 | 13.225719 | 12.301152 | 4.159906 | 3.435726 | 11.006299 | 9.462059 | 5.198780 | 3.155195 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | N16 | digital_ok | nn Shape | 14.070386 | 12.688878 | 14.070386 | 3.610557 | 4.350849 | 9.331436 | 11.338970 | 2.543767 | 4.798720 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | N16 | digital_ok | nn Shape | 13.629808 | 13.629808 | 12.546616 | 4.337351 | 3.661035 | 10.885009 | 9.530805 | 3.908450 | 2.487174 |